面向多元函数型删失分位数回归的相似性引导迁移学习
Similarity-Informed Transfer Learning for Multivariate Functional Censored Quantile Regression
AI总结：
针对目标器官移植中心样本少、隐私约束强的问题，提出基于队列相似性灵活迁移源队列信息的SITL方法，用于多元函数型删失分位数回归，兼具理论保障与更优的估计推断性能。
摘要：
为解决在样本量有限且数据共享隐私约束严格的目标移植中心（目标队列）中，利用其他器官移植中心的患者数据（源队列）提升生存时间估计与推断效果的难题，我们提出了相似性引导迁移学习（Similarity-Informed Transfer Learning，SITL）方法。该方法通过根据各源队列与目标队列的相似性灵活利用其信息，实现多元函数型删失分位数回归的估计，且可适配持续更新的实时数据。我们建立了SITL方法所得估计量的渐近性质，证明其具有更优的收敛速度。此外，我们开发了一种增强方法，将SITL方法与重采样技术结合，为函数型系数构建更精准的置信区间，并提供了理论保障。大量模拟研究以及在肾移植数据上的应用验证了SITL方法的显著优势。与仅依赖目标队列或不加区分地合并源队列与目标队列数据的方法相比，SITL方法大幅提升了估计与推断的性能。
英文摘要：
To address the challenge of utilizing patient data from other organ transplant centers (source cohorts) to improve survival time estimation and inference for a target center (target cohort) with limited samples and strict data-sharing privacy constraints, we propose the Similarity-Informed Transfer Learning (SITL) method. This approach estimates multivariate functional censored quantile regression by flexibly leveraging information from each source cohort based on its similarity to the target cohort. Furthermore, the method is adaptable to continuously updated real-time data. We establish the asymptotic properties of the estimators obtained using the SITL method, demonstrating improved convergence rates. Additionally, we develop an enhanced approach that combines the SITL method with a resampling technique to construct more accurate confidence intervals for functional coefficients, backed by theoretical guarantees. Extensive simulation studies and an application to kidney transplant data illustrate the significant advantages of the SITL method. Compared to methods that rely solely on the target cohort or indiscriminately pool data across source and target cohorts, the SITL method substantially improves both estimation and inference performance.
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